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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Adaptive-LIO: Enhancing Robustness and Precision Through Environmental Adaptation in LiDAR Inertial Odometry

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IEEE Internet of Things Journal 2025年第9期12卷 12123-12136页

作者： Zhao, Chengwei Hu, Kun Xu, Jie Zhao, Lijun Han, Baiwen Wu, Kaidi Tian, Maoshan Yuan, Shenghai Harbin Institute of Technology State Key Laboratory of Robotics and Systems Heilongjiang Harbin150001 China Hangzhou Qisheng Intelligent Techology Company Ltd. Zhejiang Hangzhou311217 China China University of Mining and Technology School of Mechatronic Engineering Jiangsu Xuzhou221116 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West 639798 Singapore University of Electronic Science and Technology School of Mechanical and Electrical Engineering Sichuan Chengdu611731 China

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, LiDAR inertial odometry (LIO) becomes increasingly prevalent in robotics and autonomous driving. However, many current SLAM systems lack sufficient adaptability to various scenarios. Challenges include decreased point cloud accuracy with longer frame intervals under the constant velocity assumption, coupling of erroneous IMU information when IMU saturation occurs, and decreased localization accuracy due to the use of fixed-resolution maps during indoor-outdoor scene transitions. To address these issues, we propose a loosely coupled adaptive LIO named Adaptive-LIO, which incorporates adaptive segmentation to enhance mapping accuracy, adapts motion modality through IMU saturation and fault detection, and adjusts map resolution adaptively using multiresolution voxel maps based on the distance from the LiDAR center. Our proposed method has been tested in various challenging scenarios, demonstrating the effectiveness of the improvements we introduce. The code is open-source on GitHub: Adaptive-LIO. © 2014 IEEE.

关键词： SLAM robotics

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Asynchronous PID Control for T-S Fuzzy systems Over Gilbert-Elliott Channels Utilizing Detected Channel Modes

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IEEE Transactions on Fuzzy systems 2025年第5期33卷 1555-1567页

作者： Wang, Yezheng Wang, Zidong Zou, Lei Ge, Quanbo Dong, Hongli Nanjing University of Information Science and Technology School of Automation Nanjing210044 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Donghua University College of Information Science and Technology Shanghai201620 China Ministry of Education Engineering Research Center of Digitalized Textile and Fashion Technology Shanghai201620 China Nanjing University of Information Science and Technology Jiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent Systems Nanjing210044 China Nanjing210044 China Northeast Petroleum University State Key Laboratory of Continental Shale Oil Daqing163318 China Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

This article is concerned with the H∞ proportional-integral-derivative (PID) control problem for Takagi-Sugeno fuzzy systems over lossy networks that are characterized by the Gilbert-Eillott model. The communication quality is reflected by the presence of two channel modes (i.e., "bad" mode and "good" mode), which switch randomly according to a Markov process. In the "bad" mode, packet dropouts are governed by a stochastic variable sequence. Considering the inaccessibility of channel modes, a mode detector is utilized to estimate the communication situation. The relationship between the actual channel mode and the estimated mode is depicted in terms of certain conditional probabilities. Moreover, a comprehensive model is constructed to represent the probability uncertainties arising from statistical errors in channel mode switching, packet dropouts, and mode detection processes. Subsequently, a robust asynchronous PID controller, based on the detected channel mode, is proposed. Sufficient conditions are then derived to ensure the mean-square stability of the closed-loop system while maintaining the desired H∞performance. Finally, the efficacy of the proposed design approach is demonstrated through a simulation example. © 1993-2012 IEEE.

关键词： Markov processes

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Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks

Time-independent Spiking Neuron via Membrane Potential Estim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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An Adaptive Control Method for Ship Speed in Restricted Waters

An Adaptive Control Method for Ship Speed in Restricted Wate...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Wu, Fanglie Wu, Bing Xiong, Mai Su, Xin Jiang, Jinhui State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China Wuhan University of Technology Wuhan430056 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430056 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Academician Workstation of China COSCOSHIPPINGCO Ltd. Wuhan China

ISBN: (纸本)9789819639687

In inland waterway transportation, ships often experience incidents such as groundings and other navigational accidents as a result of failing to adhere to safe sailing speed. An adaptive controller for ship speed is designed under the dual constraints of water depth and tidal time when entering the port by tide. First, the factors are identified that affect the safe speed of ships and the Barrass II formula is introduced to determine the relationship between ship subsidence and maximum safe speed. Second, a 4-DOF ship motion model is established, and a dynamic safety speed constraint framework is constructed based on requirements such as ship speed, ship posture, Under Keel Clearance (UKC) and tidal time. Finally, dynamic constraints and nonlinear model predictive control (MPC) methods are combined, safety speed constraints and minimum speed constraints are designed, and a speed adaptive control model is established. The experiment shows that the controller can effectively track the target trajectory under the constraint of safe speed, the objective of safe navigation is achieved. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Inland waterways

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Predicting miRNA-Disease Associations via Meta-Path Embedding

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 911-922页

作者： Lei Wang Xun Huang Xueming Liu The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

MicroRNAs (miRNAs) play a critical role in various biological processes, making the accurate identification of miRNA-disease associations essential for understanding human diseases. Existing methods for predicting these associations often lack interpretability. In this study, we introduce MNEMDA, a novel computational method that leverages meta-path-based network embedding (metapath2vec) to extract features from a heterogeneous network of miRNA-disease interactions. Using the XGBoost classifier, MNEMDA predicts potential miRNA-disease associations. Our approach achieves an overall AUC of 0.959 and an overall AUPR of 0.952, outperforming several state-of-the-art models with an average AUC score of 0.958 across 15 common diseases. Case studies on renal cell carcinoma and breast neoplasms confirm MNEMDA's efficacy, with 93% and 97% of the top 30 predicted miRNAs, respectively, validated by recent experimental reports. Survival analysis also verifies the effectiveness and practical value of MNEMDA. Additionally, MNEMDA maintains excellent performance on other datasets, demonstrating its generalization ability. Moreover, MNEMDA shows insensitivity to input data noise, providing a robust and interpretable tool for predicting miRNA-disease associations.

关键词： Diseases Vectors Semantics Predictive models Heterogeneous networks Biological system modeling Feature extraction Databases Data models Data mining

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PDDepth: Pose Decoupled Monocular Depth Estimation for Roadside Perception System

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Huanan Zhang, Xinyu Chen, Zhengxian Jun, Li Liu, Huaping School of Vehicle and Mobility Tsinghua University Beijing100084 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China

Accurate depth information is crucial for roadside perception in Cooperative Vehicle Infrastructure systems. Beyond existing radar and LiDAR solutions, monocular depth estimation using surveillance cameras is emerging as a superior approach due to its cost-effectiveness and dense depth output. Unlike onboard cameras, roadside cameras are relatively fixed in position. Many existing monocular depth estimation methods, which do not independently model camera pose, tend to overfit to training data and produce suboptimal results when confronted with slight variations in camera poses, which may be caused by external forces within the same camera or across different cameras. To address this issue, a pose decoupled monocular depth estimation method specifically designed for roadside perception systems is proposed. This method separates depth estimation into two components: a pose-dependent modeling portion that recovers ground depth based on the current camera pose, and a pose-agnostic portion that estimates pixel height relative to the ground plane. Additionally, a knowledge distillation framework is introduced to improve the robustness of the proposed method against variations in roadside cameras. To validate the method, we propose the first open source dataset for roadside monocular depth estimation, DAIR-MDE, and a roadside instance segmentation dataset, DAIR-Ins, both derived from the DAIR dataset. The proposed method demonstrates significant advances over the state-of-the-art methods on DAIR-MDE. The proposed dataset and source code are publicly available at https://***/441599828/PDDepth. ©1991-2012 IEEE.

关键词： Roadsides

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Predictor-Based Time Delay Control of A Hex-Jet Unmanned Aerial Vehicle

arXiv

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arXiv 2025年

作者： Liang, Junning Zheng, Haowen Zhang, Yuying Gao, Yongzhuo Dong, Wei Lyu, Ximin School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

Turbojet-powered VTOL UAVs have garnered increased attention in heavy-load transport and emergency services, due to their superior power density and thrust-to-weight ratio compared to existing electronic propulsion systems. The main challenge with jet-powered UAVs lies in the complexity of thrust vectoring mechanical systems, which aim to mitigate the slow dynamics of the turbojet. In this letter, we introduce a novel turbojet-powered UAV platform named Hex-Jet. Our concept integrates thrust vectoring and differential thrust for comprehensive attitude control. This approach notably simplifies the thrust vectoring mechanism. We utilize a predictor-based time delay control method based on the frequency domain model in our Hex-Jet controller design to mitigate the delay in roll attitude control caused by turbojet dynamics. Our comparative studies provide valuable insights for the UAV community, and flight tests on the scaled prototype demonstrate the successful implementation and verification of the proposed predictor-based time delay control technique. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Student academic performance prediction via hypergraph and TabNet

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Journal of Big Data 2025年第1期12卷 1-24页

作者： Yang, Yisheng Zhao, Shiyu An, Sufang Li, Xiaoyong Zhang, Yong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China Academic Affairs Office Beijing Open University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Student academic performance prediction plays a very important role in student management. With the development of deep learning, there has been some related research work on predicting student academic performance. Although many factors influence students’ academic performance, students at the same university tend to have similar intellectual levels and learning environments. Therefore, learning behaviors can significantly impact their academic performance. Thus, modeling students’ behavior can enable the prediction of their academic performance. However, existing methods mostly focus on modeling individual student behaviors, neglecting the complex associations among students hidden within campus behavioral data. In reality, the associations between students often involve higher-order, multi-to-multi relationships, rather than simple, pairwise connections. At the same time, most data-driven deep learning models are not interpretable. But in fact, the analysis of behaviors that affect student academic performance is more useful in many cases. To address these issues, this paper proposes a student academic performance prediction model that combines hypergraphs and TabNet. This method first processes and extracts usable behavior features from collected multi-source campus behavior data; secondly, it utilizes K-Nearest Neighbors (KNN) to construct a hypergraph to describe the higher-order associations among students; then, it uses hypergraph convolution to aggregate neighborhood features to learn sample embedding representations; finally, the academic performance of students are predicted by the TabNet model. Experimental results on real student behavior datasets indicate that the precision, recall, and F1 score of the method proposed in this paper are improved compared to baseline methods. Additionally, using hypergraphs and TabNet can help to explain the relationship between student behavior and performance at the feature level.

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A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving

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Autonomous intelligent systems 2025年第1期5卷 1-15页

作者： Fan, Rui Li, Jiahang Li, Jiaqi Wang, Jiale Long, Ziwei Jia, Ning Liu, Yanan Wang, Wenshuo Bocus, Mohammud J. Vityazev, Sergey Chen, Xieyuanli Xiao, Junhao Andreev, Stepan Lu, Huimin Dvorkovich, Alexander College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and the Frontiers Science Center for Intelligent Autonomous Systems Tongji University 4800 Caoan Rd Shanghai201804 China College of Electronics & Information Engineering Tongji University 4800 Caoan Rd Shanghai201804 China School of Microelectronics Shanghai University 20 Chengzhong Rd Shanghai201800 China School of Mechanical Engineering Beijing Institute of Technology 5 Zhongguancun South Street Beijing100081 China Department of Electrical and Electronic Engineering University of Bristol Woodland Road England BristolBS8 1UB United Kingdom Faculty of Radio Engineering and Telecommunications Ryazan State Radio Engineering University Ulitsa Gagarina 59/1 Ryazan Oblast Ryazan390005 Russia College of Intelligence Science and Technology National University of Defense Technology 109 Deya Road Changsha410073 China Microwave Photonics Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia Multimedia Technology and Telecom Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia

Road scene parsing is a crucial capability for self-driving vehicles and intelligent road inspection systems. Recent research has increasingly focused on enhancing driving safety and comfort by improving the detection of both drivable areas and road defects. This article reviews state-of-the-art networks developed over the past decade for both general-purpose semantic segmentation and specialized road scene parsing tasks. It also includes extensive experimental comparisons of these networks across five public datasets. Additionally, we explore the key challenges and emerging trends in the field, aiming to guide researchers toward developing next-generation models for more effective and reliable road scene parsing. © The Author(s) 2025.

关键词： Semantics

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